In recent years, finance has had an explosion in passive investing. Passive investing means that you invest in a basket of assets that’s called an exchange-traded fund (ETF). This way, you don’t spend time researching individual stocks or companies or take the risk of investing in a single stock. ETFs offer more diversification.
The ETF Analyzer is a database application showing how any asset in the ETF portfolio can be analyzed using the cumulative return and the annualized average returns. In the Fintech world, it is quite a common use case to analyze the asset performance in the ETF portfolio.
The ETF Analyzer largely does the following tasks in the etf.db, a sample database.
- Show analysis of the ETF data that a SQL database stores in SQLite
- Plot professionally styled and formatted interactive visualizations
If you look inside 'Images\..' folder, two screenshots are attached to show how a lightweight web page can be rendered by deploying the Jupyter Notebook via the Voilà library.
This project leverages python 3.7.* with the following additional packages:
- Jupyter Notebook - The main module of the Financial Planner is written in Jupyter Notebook.
- Conda - Conda environment is recommended to have Pandas library and other dependencies pre-installed.
Required Libraries:
You may need the following libraries to work with the program.
- Pandas - pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.
- hvPlot - hvPlot provides a high-level plotting API built on HoloViews that provides a general and consistent API for plotting data in Python.
- SQLAlchemy - SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL.
To use the ETF Analyzer application, clone the repository and run the etf_analyzer.ipynb with Jupyter notebook.
From the Git Bash terminal, make sure to 'activate conda' and appropriate virtual enivorment. Next, launch the JupyterLab web-based interactive development environment (IDE) interface by typing at the prompt:
> jupyter labThen, browse to etf_analyzer.ipynb starter code file to run the program. See the image below for a quick hint.
Similarly, the Jupyter Notebook output can also be rendered as a web page by deploying the Jupyter Notebook file etf_analyzer.ipynb as a web application.
From the Terminal (git bash or python based), type the following command to launch the web page:
> voila .\etf_analyzer.ipynb
Voila web page view.
You may see the Jupyter Notebook output as a plain HTML without Code Cells or Markdown attributes.
FinTech Labs, Inc.
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